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15_sklearn_stacking.py
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15_sklearn_stacking.py
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# -*- coding: utf-8 -*-
from heamy.dataset import Dataset
from heamy.estimator import Regressor, Classifier
# ModelsPipeline：https://blog.csdn.net/qiqzhang/article/details/85477242 ； https://cloud.tencent.com/developer/article/1463294
from heamy.pipeline import ModelsPipeline
import pandas as pd
import xgboost as xgb
import datetime
from sklearn.metrics import roc_auc_score
# lightgbm安装：https://blog.csdn.net/weixin_41843918/article/details/85047492
# lgb样例：https://www.jianshu.com/p/c208cac3496f
import lightgbm as lgb
from sklearn.preprocessing import LabelEncoder,OneHotEncoder
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
import numpy as np
from pandas.core.frame import DataFrame
from gen_feas import load_data
def xgb_feature(X_train, y_train, X_test, y_test=None):
other_params = {'learning_rate': 0.125, 'max_depth': 3}
model = xgb.XGBClassifier(**other_params).fit(X_train, y_train)
predict = model.predict_proba(X_test)[:,1]
#minmin = min(predict)
#maxmax = max(predict)
#vfunc = np.vectorize(lambda x:(x-minmin)/(maxmax-minmin))
#return vfunc(predict)
return predict
def xgb_feature2(X_train, y_train, X_test, y_test=None):
# , 'num_boost_round':12
other_params = {'learning_rate': 0.1, 'max_depth': 3}
model = xgb.XGBClassifier(**other_params).fit(X_train, y_train)
predict = model.predict_proba(X_test)[:,1]
#minmin = min(predict)
#maxmax = max(predict)
#vfunc = np.vectorize(lambda x:(x-minmin)/(maxmax-minmin))
#return vfunc(predict)
return predict
def xgb_feature3(X_train, y_train, X_test, y_test=None):
# , 'num_boost_round':20
other_params = {'learning_rate': 0.13, 'max_depth': 3}
model = xgb.XGBClassifier(**other_params).fit(X_train, y_train)
predict = model.predict_proba(X_test)[:,1]
#minmin = min(predict)
#maxmax = max(predict)
#vfunc = np.vectorize(lambda x:(x-minmin)/(maxmax-minmin))
#return vfunc(predict)
return predict
def rf_model(X_train, y_train, X_test, y_test=None):
# n_estimators = 100
model = RandomForestClassifier(n_estimators=90, max_depth=4,random_state=10).fit(X_train,y_train)
predict = model.predict_proba(X_test)[:,1]
#minmin = min(predict)
#maxmax = max(predict)
#vfunc = np.vectorize(lambda x:(x-minmin)/(maxmax-minmin))
#return vfunc(predict)
return predict
def et_model(X_train, y_train, X_test, y_test=None):
model = ExtraTreesClassifier(max_features = 'log2', n_estimators = 1000 , n_jobs = -1).fit(X_train,y_train)
return model.predict_proba(X_test)[:,1]
def gbdt_model(X_train, y_train, X_test, y_test=None):
# n_estimators = 700
model = GradientBoostingClassifier(learning_rate = 0.02, max_features = 0.7, n_estimators = 100 , max_depth = 5).fit(X_train,y_train)
predict = model.predict_proba(X_test)[:,1]
#minmin = min(predict)
#maxmax = max(predict)
#vfunc = np.vectorize(lambda x:(x-minmin)/(maxmax-minmin))
#return vfunc(predict)
return predict
def logistic_model(X_train, y_train, X_test, y_test=None):
model = LogisticRegression(penalty = 'l2').fit(X_train,y_train)
return model.predict_proba(X_test)[:,1]
def lgb_feature(X_train, y_train, X_test, y_test=None):
model = lgb.LGBMClassifier(boosting_type='gbdt', min_data_in_leaf=5, max_bin=200, num_leaves=25, learning_rate=0.01).fit(X_train, y_train)
predict = model.predict_proba(X_test)[:,1]
#minmin = min(predict)
#maxmax = max(predict)
#vfunc = np.vectorize(lambda x:(x-minmin)/(maxmax-minmin))
#return vfunc(predict)
return predict
VAILD = False
if __name__ == '__main__':
if VAILD == False:
train, test, no_features, features = load_data()
train_train = train[features].values
y = train['target'].astype('int32')
test_data = test[features].values
print(train_train.shape)
train_train_x = train_train
test_test_x = test_data
xgb_dataset = Dataset(X_train=train_train_x,y_train=train['target'],X_test=test_test_x,y_test=None,use_cache=False)
#heamy
print ("---------------------------------------------------------------------------------------)")
print ("开始构建pipeline：ModelsPipeline(model_xgb,model_xgb2,model_xgb3,model_lgb,model_gbdt)")
model_xgb = Regressor(dataset=xgb_dataset, estimator=xgb_feature,name='xgb',use_cache=False)
model_xgb2 = Regressor(dataset=xgb_dataset, estimator=xgb_feature2,name='xgb2',use_cache=False)
model_xgb3 = Regressor(dataset=xgb_dataset, estimator=xgb_feature3,name='xgb3',use_cache=False)
model_gbdt = Regressor(dataset=xgb_dataset, estimator=gbdt_model,name='gbdt',use_cache=False)
model_lgb = Regressor(dataset=xgb_dataset, estimator=lgb_feature,name='lgb',use_cache=False)
model_rf = Regressor(dataset=xgb_dataset, estimator=rf_model,name='rf',use_cache=False)
# pipeline = ModelsPipeline(model_xgb,model_xgb2,model_xgb3,model_lgb,model_gbdt, model_rf)
pipeline = ModelsPipeline(model_xgb, model_xgb2, model_xgb3, model_lgb, model_rf)
print ("---------------------------------------------------------------------------------------)")
print ("开始训练pipeline：pipeline.stack(k=7, seed=111, add_diff=False, full_test=True)")
stack_ds = pipeline.stack(k=7, seed=111, add_diff=False, full_test=True)
# k = 7 model_xgb, model_xgb2, model_xgb3, model_lgb, model_rf : AUC: 0.780043
print ("stack_ds: ", stack_ds)
print ("---------------------------------------------------------------------------------------)")
print ("开始训练Regressor：Regressor(dataset=stack_ds, estimator=LinearRegression,parameters={'fit_intercept': False})")
stacker = Regressor(dataset=stack_ds, estimator=LinearRegression,parameters={'fit_intercept': False})
print ("---------------------------------------------------------------------------------------)")
print ("开始预测：")
predict_result = stacker.predict()
id_list = test["id"].tolist()
d ={ "id" : id_list, "target" : predict_result }
res = DataFrame(d)#将字典转换成为数据框
print (">>>>", res)
csv_file = 'result/res_stacking.csv'
res.to_csv( csv_file,index=None )